Instructions to use WindyTranslate/translate-sv-bg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-bg with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="WindyTranslate/translate-sv-bg")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-bg") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-bg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote herm0 build from WindstormLabs/translate-sv-bg, with attribution and change statement
d69040f verified - Xet hash:
- 78fdc3d52a49f6ab528d2b79f5a81d72b533274dccdd35ebc00c5635d785f2a2
- Size of remote file:
- 811 kB
- SHA256:
- b5473ebe959daf66daafe34f93370f44f618a78e0b2f8a6812f93e65e4277319
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.